Supervised Learning-based Sound Source Distance Estimation Using Multivariate Features

Kalamkas Zhagyparova, Ruslan Zhagypar, Amin Zollanvari, Muhammad Tahir Akhtar

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations


This paper introduces the use of supervised machine learning methods with a combination of several sound source distance-dependent features to tackle the problem of distance-of-arrival (DisOA) estimation. The DisOA estimation is approached as a classification problem, which aims to classify a recorded audio signal into one of the predefined four DisOA classes regardless of the orientation angle. The datasets for both training and testing purposes are simulated by convolving appropriate room impulse responses with anechoic speech signals. The performance of three conventional and efficient classifiers was examined along with various subsets of four extracted features including: 1) Diffuseness (DIFF); 2) Binaural spectral magnitude difference standard deviation (BSMD-STD); 3) Magnitude squared coherence (MSC); and 4) Direct-to-reverberant ratio (DRR). The simulations consider the use of different source signals as well as varying directions-of-arrival and the room sizes. Our empirical results show that the use of a single univariate feature, namely, MSC, along with K-nearest neighbor (KNN) could potentially lead to an accurate DisOA classification rule.

Original languageEnglish (US)
Title of host publicationTENSYMP 2021 - 2021 IEEE Region 10 Symposium
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665400268
StatePublished - Aug 23 2021
Event2021 IEEE Region 10 Symposium, TENSYMP 2021 - Jeju, Korea, Republic of
Duration: Aug 23 2021Aug 25 2021

Publication series

NameTENSYMP 2021 - 2021 IEEE Region 10 Symposium


Conference2021 IEEE Region 10 Symposium, TENSYMP 2021
Country/TerritoryKorea, Republic of


  • Acoustic Distance Estimation
  • KNN
  • LDA
  • NMC
  • Sound Source Localization

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Information Systems and Management
  • Computer Networks and Communications
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering
  • Instrumentation


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